AI-102 Implement an agentic solution Practice Question
You are building an agent with Azure AI Agent Service. The agent must consult a product catalog stored in an Azure AI Search index before answering questions, and you want the retrieval to happen automatically on every run without writing any orchestration code. You have already created the index and a project connection to it. Which configuration should you apply to the agent?
⚠ Common exam trap
The trap here is assuming the model can reach an external search endpoint from instructions alone, when retrieval must be exposed as a tool backed by a project connection.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Attach the Azure AI Search index as a knowledge tool on the agent and let the service invoke it during runs.
Attaching the Azure AI Search index as a knowledge tool is the native way Azure AI Agent Service performs retrieval-augmented generation. The service manages query formulation, execution against the index through the project connection, and injection of results into the model context, satisfying the no-orchestration-code requirement while reusing the existing index.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable grounding by uploading the catalog documents to the agent's file storage and referencing them in the system message.
Why it's wrong here
File-based grounding is a different retrieval path and does not use your existing Azure AI Search index. It also would not keep the agent synchronized with the indexed catalog, and the scenario states the index already exists. File search is chosen when documents are not already indexed in a search service.
- ✓
Attach the Azure AI Search index as a knowledge tool on the agent and let the service invoke it during runs.
Why this is correct
Knowledge tools are the built-in retrieval mechanism for Azure AI Agent Service. Once the search index is attached as a knowledge tool with a valid project connection, the service decides when to query it and injects the retrieved passages into the model context, so no application-side orchestration code is required for the automatic retrieval behavior described.
- ✗
Create a function tool that calls the Azure AI Search REST API and register it as a tool on every run.
Why it's wrong here
A function tool requires your application to execute the function and return its output to the run, which means you own the orchestration loop. That contradicts the requirement that retrieval happen automatically without custom code. Function tools are appropriate when you need custom logic, not for standard index-backed retrieval.
- ✗
Add the search index endpoint and admin key to the agent's instructions so the model can call it directly.
Why it's wrong here
Instructions are natural-language guidance for the model; they do not grant network access or authentication. The model cannot call an Azure AI Search endpoint by itself, and embedding an admin key in instructions is both ineffective and a serious secret-exposure risk. Retrieval must be wired through a tool and a project connection.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.